Transcript of "Forestry Bioenergy in the Southeast United States: Implications for Wildlife Habitat and Biodiversity"

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Forestry Bioenergy in the Southeast United States:
Implications for Wildlife Habitat and Biodiversity
Final Report
December 23, 2013

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ACKNOWLEDGMENTS
This study was commissioned by the National Wildlife Federation and Southern Environmental Law Center with
funds provided by Doris Duke Charitable Foundation.
Patient project management was provided by F.G. Courtney-Beauregard, Julie Sibbing, Ben Larson, and Aviva Glaser
of the National Wildlife Federation, as well as David Carr and Derb Carter from Southern Environmental Law Center. Bruce Stein and Barbara Bramble from National Wildlife Federation provided important suggestions and contributions in technical review that greatly improved the ﬁnal report. Jovian Sackett from Southern Environmental Law
Center provided key GIS datasets and other insights that also were critical to project development and completion.
We greatly thank Jacquie Bow, Kristin Snow, Jason McNees, and Leslie Honey of NatureServe for assistance in conducting and interpreting overlay analyses of at-risk (G1-G3) ecological associations. Additional assistance in developing G1-G3 analyses was provided by Matt Elliott, Anna Yellin, and John Ambrose at the Georgia Natural Heritage
Program; John Finnegan from the North Carolina Natural Heritage Program; and Kirsten Hazler and Karen Patterson of the Virginia Natural Heritage Program.
Research for this project was conducted through a collaborative effort between faculty and graduate student researchers at the University of Georgia, University of Florida, and Virginia Polytechnic Institute and State University (a.k.a.,
Virginia Tech University). Chapter 2, authored by Daniel Geller (University of Georgia, College of Engineering) and
Jason M. Evans (University of Georgia, Carl Vinson Institute of Government), provides an overview of facilities chosen for the study’s focus. Chapter 3, authored by Divya Vasudev, Miguel Acevedo, and Robert J. Fletcher, Jr. (all from
University of Florida, Department of Wildlife Ecology and Conservation), provides a presentation of conservation
analysis methods and identiﬁcation of indicator species. Chapter 4, authored by Jason M. Evans, provides a technical explanation of spatial modeling methods employed for the facility case studies. Chapters 5-10, authored by Jason
M. Evans, Alison L. Smith (University of Georgia, College of Environment and Design), Daniel Geller, Jon Calabria
(University of Georgia, College of Environment and Design), Robert J. Fletcher, Jr., and Janaki Alavalapati (Virginia
Tech University, Department of Forest Resources and Environmental Conservation) provide the results and discussion of facility case study analyses. Chapter 11, authored by Pankaj Lal (Montclair State University, Department of
Earth and Environmental Studies), Thakur Upadhyay (Virginia Tech University, Department of Forest Resources and
Environmental Conservation) and Janaki Alavalapati, provides an overview of forestry biomass energy policies within
state, federal, and international contexts, as well as the increasing policy attention to biodiversity concerns. Chapter 12,
co-authored by all investigators, synthesizes the results of the report into a series of suggestions for policy consideration and future research studies. Executive Summary and Final Report layout completed by Alison L. Smith.
Christopher Stebbins, a graduate student at the University of Georgia’s College of Environment and Design, provided
key technical support for developing a number of GIS analyses and map designs. Robinson Schelhas, an undergraduate intern at the University of Georgia, provided tireless assistance with developing maps, assembling literature databases, formatting tables, and taking numerous photographs. Sumner Gann, a graduate student at the University of
Georgia’s College of Environment and Design, provided document layout and formatting assistance.
Recommended citation: Evans, J.M., R.J. Fletcher, Jr., J.R.R. Alavalapati, A.L. Smith, D. Geller, P. Lal, D. Vasudev, M.
Acevedo, J. Calabria, and T. Upadhyay. 2013. Forestry Bioenergy in the Southeast United States: Implications for Wildlife Habitat
and Biodiversity. National Wildlife Federation, Merriﬁeld, VA.
Cover photo credit:Tiffany Williams Woods

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Forestry Bioenergy in the Southeast United States: Implications for Wildlife Habitat and Biodiversity
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I. INTRODUCTION
Spanning across the low-lying and sandy
soils of the Coastal Plain, the gentle slopes
and clay soils of the Piedmont, and the
steep sloping terrains of the southern Appalachian Mountains, the forests of the
southeastern (SE) U.S. are widely recognized for their high biodiversity. Differentiated across the region by various terrains,
precipitation patterns, annual temperature
ranges, and dominant tree species, SE forests broadly share a wet and humid climate
with mild winters that produce minimal to
no persistent snow cover in even the coldest
locations. These favorable climate conditions support high primary forest productivity as compared to most other U.S. forest
regions and similar temperate latitudes
across the world. This high productivity and
terrain heterogeneity together support the
wide diversity of ecological associations and
wildlife habitats found throughout the SE
region.
Land cover change and management factors have prompted signiﬁcant population and range area declines for a number
of native forest-dependent plants and
animals throughout the SE over the past
two centuries. Speciﬁc factors that have
served as primary stressors to native forest biodiversity in the SE region include:
1) historic logging of virtually all original
primary forests; 2) large-scale clearing of
primary and naturally regenerated forests
for conversion into agriculture, plantation
pine forestry, and suburban development; 3)
long-term suppression of ﬁre from forest
ecosystems dependent on this disturbance;
and 4) establishment and spread of various
invasive plants, animals, and pathogens (see,
e.g., Martin 1993; Grifﬁth et al. 2003). But
despite these direct habitat stressors and additional secondary effects from large-scale
habitat fragmentation, today’s SE forest
landscape still contains large areas of high
quality habitat that together support the vast
majority of native plant and wildlife species
originally found in the region at the time of
European discovery (Trani 2002).
This study was commissioned jointly by the
National Wildlife Federation and Southern
Environmental Law Center for the purpose
of developing and discussing scenario-based
assessments of wildlife habitat risks from
the woody biomass to bioenergy industry in
the SE U.S. The rationale behind the study
is that the SE U.S. forest region – which
the U.S. Forest Service deﬁnes as including
the forested areas of Alabama, Arkansas,
Florida, Georgia, Kentucky, Louisiana, Mississippi, North Carolina, Oklahoma South
Carolina, Tennessee, Texas, and Virginia – is
currently experiencing what is perhaps the
world’s most rapid growth in the development of woody biomass production facilities (Mendell and Lang 2012). According
to recent estimates by Forisk Consulting
(2013), U.S. wood pellet production may exceed 13.7 million tons in 2014, representing
an 87% increase from 2012 and with most
of this production likely being supplied by
forests of the SE U.S. Due mostly to ongoing renewable energy mandates in the EU
being implemented under the Kyoto Accord, some analysts expect similar demand
increases for SE wood pellets to continue
through 2020 and beyond (Goh et al. 2013).
Opportunities and Risks
Expansion of the bioenergy industry is
prompting wide-ranging discussion about

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Forestry Bioenergy in the Southeast United States: Implications for Wildlife Habitat and Biodiversity
opportunities and risks that new biomass
energy demands may have on SE forest
lands. Available research suggests that evaluation of woody biomass energy is highly
complex, and that many kinds of environmental tradeoffs are implied by biomass
utilization scenarios. These tradeoffs can be
expected to vary signiﬁcantly across different contexts of place, spatio-temporal scale,
and intensity of resource utilization (see,
e.g., Talbot and Ackerman 2009).
This makes any generalizations about future
impact difﬁcult to impossible across a
region as large and diverse as the SE U.S.
However, a summary of such tradeoffs
under an opportunities and risk framework
is useful for summarizing the complexity of
discussions regarding the ongoing development of this industry, and the variety
of ways that these discussions speciﬁcally
interplay with concerns about biodiversity
conservation.
Opportunities
It has been widely argued that emergence
of a new energy market for lower quality biomass material may incentivize wider
implementation of management practices
generally viewed as beneﬁcial to the forest
landscape and associated ecological systems.
For example, new energy users have been
suggested as a potential market for deadwood and understory overgrowth materials that pose high risks for catastrophic
wildﬁre, but are otherwise uneconomical to
remove (Evans and Finkral 2009; Susaeta
et al. 2009). Research suggests that regular thinning of many SE plantation forest
landscapes, particularly when coupled with
prescribed burning interventions, can result
in rapid positive responses for a wide variety
native taxa, including many species of
conservation concern (Hedman et al. 2000;
Miller et al. 2009). Direction of undesir-
able and invasive plant material to biomass
energy facilities is also sometimes noted as
a potential catalyst in support of large-scale
ecosystem restoration and wildlife enhancement objectives (Eisenbees et al. 2009;
Evans 2010; Spears 2012).
From a broader environmental standpoint,
even the most intensive SE forestry systems
require relatively small human energy inputs
in the form of fertilizer, pesticides, herbicides, and fuel as compared to common
agricultural bioenergy feedstocks such as
corn, sugarcane, and soy beans (Evans and
Cohen 2009; Daystar et al. 2012; Dwivedi et
al. 2012). By extension, comparative analyses generally show signiﬁcant ecosystem
service advantages for forestry biomass in
terms of long-term carbon cycling, nutrient
processing, water quality protection, and
water quantity regulation as compared to
traditional agricultural feedstocks (Dwivedi
et al. 2009; Hsu et al. 2010; Lippke et al.
2011). A variety of research indicates that
site-level biodiversity values from intensive
plantation forestry land covers in the SE
U.S. are generally higher than those associated with other human-modiﬁed landscapes
(Brockerhoff et al. 2008; Miller et al. 2009),
including ﬁrst generation agricultural bioenergy feedstocks (Fletcher et al. 2010).
Risks
Recent literature lists several ways that largescale woody bioenergy development has
the potential to impact ecological systems
in adverse ways. First, there is increasing
recognition that rapid scale-up of bioenergy facilities in the SE forest landscape
likely implies a level of demand that greatly
exceeds the feasible supply of lower quality
and/or waste materials (Galik et al. 2009),
which were once regarded as a primary
available source (e.g., Perlack et al. 2005).
By extension, it is worried that such a large

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Forestry Bioenergy in the Southeast United States: Implications for Wildlife Habitat and Biodiversity
new demand, particularly when placed on
top of existing demands for the traditional
forest products industries, may imply levels
of woody biomass extraction that could
threaten the long-term functioning and
sustainability of SE forest habitats already
under stress from multiple factors.
Additional expansion of southern plantation pine forests, which are composed of
dense row-based plantings of loblolly (Pinus
taeda) or slash (Pinus elliottii) pines, is often
cited as one potential near-term result of
increased bioenergy demand in the Coastal
Plain and Piedmont provinces (Zhang and
Polyakov 2010; Davis et al. 2012). Conversion of extant native ecosystems into
production landscapes dedicated to intensive feedstock production is widely recognized as a major risk factor associated with
increased bioenergy demands (Fargione et
al. 2008; U.S. Environmental Protection
Agency 2011), and plantation pines are speciﬁcally regarded as a primary factor in the
loss of many natural stands of SE forests
over the latter half of the twentieth century
(Allen et al. 1996). At a stand level, intensive biomass harvest of small diameter and
residual woody materials may in some cases
have the potential to increase sediment and
nutrient loads to adjacent water bodies,
particularly in the context of highly sloped,
riparian, and wetland forestry contexts
(Janowiak and Webster 2010). Increased tree
planting densities, which are often recommended for southern pine plantation systems optimized for bioenergy production,
may also have the potential to reduce net
watershed ﬂows into regional streams, lakes,
and groundwater systems due to higher net
landscape evapo-transpiration (Evans and
Cohen 2010; McLaughlin et al. 2013).
In local woodshed areas that lack high pine
plantation production potential and/or have
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speciﬁc facility demands for hardwoodbased bioenergy, woody biomass will
necessarily be sourced from primary and/
or residual biomass obtained from natural
forest stands for at least the near-term.
This is because there currently is very little
plantation-grown hardwood capacity in the
SE U.S. (Merkle and Cunningham 2011).
Speciﬁc concerns with hardwood biomass
harvest in the Appalachian Mountains and,
to an arguably lesser extent, the Piedmont
include increased opening of closed canopy
conditions and/or substantial removal of
“downed woody matter” (DWM), both of
which may lead to habitat loss for interior
forest species (Vanderberg et al. 2012). In
the Coastal Plain, hardwood based biomass
sourcing may in many cases be preferentially
sourced from ﬂoodplain and basin wetland
forests, which are generally the most productive hardwood sites (Kline and Coleman
2010). Increased stream sedimentation,
alteration of hydrologic regimes, changes in
water chemistry, and different thermal proﬁles that can effect local ﬁsh, water birds,
and aquatic invertebrates are post-harvest
concerns when sourcing wood from riparian bottomland forests in the SE Coastal
Plain (Ensign and Mallin 2001; Hutchens et
al. 2004).
Study Goals and Questions
Biodiversity conservation is widely recognized as a pillar of sustainability assessments at local, state, national and
international levels. Because bioenergy
development is speciﬁcally linked to governmental and international policy frameworks
designed to promote climate change mitigation and other sustainability goals, detailed
assessments of wildlife habitat risks associated with current bioenergy scale-ups in SE
forests is clearly appropriate and necessary
for informing adaptive policy development
at this time.

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Forestry Bioenergy in the Southeast United States: Implications for Wildlife Habitat and Biodiversity
While this report represents an objective
effort to assess biodiversity opportunities
and risks from forestry biomass energy, we
caution that wildlife responses to bioenergy
development are fundamentally nested within, and further contribute to, a highly complex suite of variables that include many
future uncertainties and unknowns. Because
of this, it is important to note that formal
consideration of all – or even most – potential habitat change factors, scenarios, and
associated ecosystem and species responses
was neither possible nor intended.
Goals
The overarching goals of this study were
fourfold.
1. To develop spatial analyses that provide
speciﬁc information about the likely
land cover base for long-term feedstock
sourcing for six woody biomass facilities.
2. To analyze potential effects of biomass
sourcing scenarios on a selection of native wildlife species identiﬁed as having
high conservation concern.
3. To review state, national, and international policies related to deployment
of biomass-based energy, with speciﬁc
focus on sustainable sourcing criteria
that pertain to wildlife habitat and biodiversity maintenance.
4. To synthesize the land cover analyses,
wildlife assessments, and policy review
as a guide for future research focus and
associated policy development.
Facilities
To operationalize the technical research
goals (1 & 2 above), we applied a case study
approach that focuses on six forestry-based
bioenergy facilities located across the SE.
These case study facilities are:
1. Georgia Biomass, LLC, a wood pellet
manufacturing facility located near Waycross, GA in the lower Atlantic Coastal
Plain.
2. Enviva Pellets Ahoskie, a wood pellet manufacturing facility located in
Ahoskie, NC in the upper Atlantic
Coastal Plain.
3. Piedmont Green Power, a biomass ﬁred
electrical generating unit located near
Barnesville, GA in the southern reaches
of the Piedmont province.
4. South Boston Energy, a biomass ﬁred
electrical generating unit located in
South Boston, VA and in the northern
reaches of the Piedmont province.
5. Carolina Wood Pellets, a wood pellet
manufacturing facility located in Otto,
NC and in the southern Appalachian
mountains.
6. Virginia Hybrid Energy Center, a coﬁred coal and biomass electrical generating unit located in St. Paul, VA and in
the southern Appalachian mountains.
These facilities were selected because they
together provide a wide cross-sampling
of SE forest types and feedstock sourcing
practices, thus giving opportunity for comparisons across a high diversity of habitats
and impact factors. The speciﬁc spatial
modeling approaches and ﬁndings are applied and presented in such a way that they
can be utilized and reﬁned for similar future
assessments of other regional bioenergy
facilities.
Research questions
In developing the case studies, we used
literature review and spatial analysis methods to address a series of speciﬁc research
questions for each facility:
1. What woodshed ecosystems are most
at risk of biomass harvest and/or land

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Forestry Bioenergy in the Southeast United States: Implications for Wildlife Habitat and Biodiversity
Page 17
cover conversion over the lifetime of
each case study facility?
2. What habitats of critically imperiled
(G1), imperiled (G2), or vulnerable
(G3) status occur within potential
woodshed sourcing areas?
3. How might different biomass sourcing
and harvesting practices be expected to
affect native forest habitats and wildlife
species of high conservation value and
concern?
4. What policies and practices are available
to mitigate and/or address conservation concerns associated with increased
biomass energy extraction from SE
forests?
sourcing under different sets of sourcing
constraints that reﬂect various protocols
for sustainable forest management criteria. Woodshed areas with public ownership status or conservation easements that
exclude extractive timber harvests were
removed from consideration for all sourcing
model scenarios. Land cover information
for all sourcing models was based on the
United States Geological Survey’s 2011 Gap
Analysis Program (GAP) National Land
Cover dataset (USGS 2011). This dataset is
designed for use in conservation planning
and assessments, which can include largescale evaluations of biomass and renewable
energy sourcing from forest ecosystems.
Technical Approach
To address research question 1), we ﬁrst utilized facility biomass demands and local forestry productivity assumptions to calculate
landscape area sourcing requirements for
each facility. These sourcing requirements
models were then used to develop spatially
explicit sourcing models.
Sourcing models were run across a standard
set of harvest intensity and biomass allocation assumptions for each facility. Results
for sourcing models based on each of these
biomass allocation assumptions were translated into maps of relative landscape risk
for biomass harvest. Five risk classes were
deﬁned through this approach: 1) High; 2)
Moderately high; 3) Moderate; 4) Moderately low; and 5) Low. Higher risk in this
context is technically deﬁned as having a
higher relative suitability for biomass sourcing based on model factors, and does not
necessarily imply vulnerability to an adverse
biodiversity impact from this sourcing.
These sourcing models take into account
two primary spatio-economic factors: 1)
Road transport distance of biomass material from the forest to the facility; and 2)
Competition with other woody biomass
consumers in the woodshed sourcing area.
Sourcing models assumed that facilities will
preferentially source from woodshed areas
that minimize costs through less road transport distance, while also minimizing bid
pressure from competing biomass facilities.
For softwood sourcing, additional modeling
consideration was given to soil type, elevation, slope, and distance to road factors that
inﬂuence land owner decisions for establishing plantation pine across the landscape.
A series of customized “scenario screens”
were run for each facility to simulate
The spatially explicit integration of these
disparate factors and constraints into biomass sourcing models is a novel research
contribution provided by this study. Specifics of the modeling scenario development
and workﬂow integration are developed in
full detail in Chapter 4.
Softwood sourcing
For plantation pine-based biomass, a series
of ﬁve scenario screens were applied for
softwood sourcing on private lands. These

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Forestry Bioenergy in the Southeast United States: Implications for Wildlife Habitat and Biodiversity
ranged from the most permissive criterion
of allowing conversion of any upland land
cover with the exception of row crops and
developed areas, to the most restrictive of
only sourcing biomass from existing plantation pine forestry land covers.
Ecosystem and wildlife habitat overlap
assessments for softwood sourcing were
performed for a subset of two intermediate
scenario screens: 1) a permissive scenario
that allowed for conversion of natural
upland forest stands into plantation pine
based on landscape factors, while assuming
no conversion of agricultural (i.e., row crop
and pasture), developed lands, or wetland
areas into plantation pine; and 2) a restrictive scenario that limited the resource base
of softwood sourcing to existing plantation
pine and other disturbed lands (i.e., harvested, cleared, and ruderal succession) that
are presumed to form the existing resource
base for extractive softwood forestry.
Hardwood sourcing
Two scenario screens were applied for
hardwood sourcing on private lands. The
permissive screen for hardwood forestry
assumed no restriction against sourcing
from wetland and riparian forests. A more
restrictive screen limited all sourcing to
upland hardwood forests, and thus allowed
no sourcing from forested wetlands. All
hardwood sourcing screens excluded agricultural (including pasture and row crop)
and developed land covers from the forestry
biomass resource base. In two woodsheds
with large areas of land held publicly by the
U.S. Forest Service, an additional screen that
allowed for sourcing from all non-Wilderness National Forest lands was compared to
a scenario screen that prohibited all sourcing from National Forests.
At risk (G1-G3) ecological associations
Research question 2) was addressed through
a partnership with NatureServe, whose
analysts conducted detailed overlay analyses of woodshed areas to identify element
occurrences of G1 (critically imperiled), G2
(imperiled), and G3 (vulnerable) ecological associations. Identiﬁcation of such at
risk (G1-G3) associations for the purpose
of avoiding adverse impacts on forest
ecosystems of high conservation value is
a component of most sustainable forest
management certiﬁcations. Intersection
analyses of G1-G3 datasets maintained by
NatureServe were performed for each facility woodshed as deﬁned by a 75-mile road
network analysis. Known conservation areas
were excluded from consideration in these
intersection analyses.
Ecosystem and wildlife assessments
To address question 3), we conducted an
overlay analysis of detailed forest ecosystem types, as deﬁned by the 2011 GAP
Land Cover dataset, with biomass sourcing
models. Following work by Fahrig (2003),
we interpreted the primary biodiversity
impact of concern as direct habitat change
risks. These risks were speciﬁcally deﬁned
through area-based sums of cumulative harvest disturbance and/or land cover conversion potential for extant forest ecosystems
over an assumed 50-year facility life time.
Available literature and information about
facility sourcing practices were utilized to
discuss a range of general ecological, biodiversity, and wildlife responses that may be
expected under biomass sourcing scenarios.
To supplement these ecosystem/land coverbased discussions, we developed additional
overlay analyses of sourcing risk models
with spatially explicit GAP distribution
datasets for nine wildlife “indicator” species
located in some or all of the facility wood-

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Forestry Bioenergy in the Southeast United States: Implications for Wildlife Habitat and Biodiversity
sheds. These species included the eastern
spotted skunk (Spilogale putorius); long-tailed
weasel (Mustela frenata); northern bobwhite
quail (Colinus virginianus); Swainson’s warbler (Limnothlypis swainsonii); brown-headed
nuthatch (Sitta pusilla); prothonotary warbler
(Protonotaria citrea); gopher frog (Lithobates
capito); northern cricket frog (Acris crepitans);
and timber rattlesnake (Crotalus horridus).
These species were selected for analysis
through an iterative process that included
consideration of several criteria: 1) diversity
of taxa; 2) regional, rather than highly local,
distribution; 3) conservation status concerns
that could likely be affected, whether positively or negatively, by biomass extraction
practices; and 4) availability of formal GAP
distribution data.
Speciﬁc methods behind ecosystem criteria
and species selection are described in Chapter 3, while overlay methods are described
in Chapter 4. Results and interpretations for
each case study woodshed are developed in
Chapters 5-10.
Policy review
A review was developed for existing sustainable forest management (SFM) certiﬁcation
programs and best management practices
(BMPs) for SE U.S. forestry systems. SFM
programs include the Forest Stewardship
Council (FSC), Sustainable Forestry Initiative (SFI), American Tree Farm System
(ATFS), and the Program on the Endorsement of Forest Certiﬁcation (PEFC),
although none of these currently have a
standalone biomass to energy certiﬁcation.
New state-level BMPs speciﬁc for biomass
energy have been developed by the State of
South Carolina, and recommendations for
implementing biomass forestry BMPs in a
manner that may mitigate habitat concerns
has been developed by the Forest Guild.
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Report Overview
Research for this project was conducted
through a collaborative effort between
faculty and graduate student researchers at
the University of Georgia, University of
Florida, and Virginia Polytechnic Institute
and State University (a.k.a., Virginia Tech
University). Chapter 2, authored by Daniel
Geller (University of Georgia, College of
Engineering) and Jason M. Evans (University of Georgia, Carl Vinson Institute
of Government), provides an overview of
facilities chosen for the study’s focus. Chapter 3, authored by Divya Vasudev, Miguel
Acevedo, and Robert J. Fletcher, Jr. (all from
University of Florida, Department of Wildlife Ecology and Conservation), provides a
presentation of conservation analysis methods and identiﬁcation of indicator species.
Chapter 4, authored by Jason M. Evans,
provides a technical explanation of spatial
modeling methods employed for the facility
case studies. Chapters 5-10, authored by
Jason M. Evans, Alison L. Smith (University
of Georgia, College of Environment and
Design), Daniel Geller, Jon Calabria (University of Georgia, College of Environment
and Design), Robert J. Fletcher, Jr., and
Janaki Alavalapati (Virginia Tech University, Department of Forest Resources and
Environmental Conservation) provide the
results and discussion of facility case study
analyses. Chapter 11, authored by Pankaj Lal
(Montclair State University, Department of
Earth and Environmental Studies), Thakur
Upadhyay (Virginia Tech University, Department of Forest Resources and Environmental Conservation) and Janaki Alavalapati, provides an overview of forestry biomass
energy policies within state, federal, and
international contexts, as well as the increasing policy attention to biodiversity concerns.
Chapter 12, co-authored by all investigators,
synthesizes the results of the report into a
series of suggestions for policy consideration and future research studies.

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Forestry Bioenergy in the Southeast United States: Implications for Wildlife Habitat and Biodiversity
energy production system for export. The
main two power plants that originally intended to source from Georgia Biomass are
Plant Amer in the Netherlands and Plant
Tilbury in the United Kingdom. However,
it has been announced that Plant Tilbury
will ceased biomass power generation in
October 2013. We identiﬁed this facility as
potentially high impact due to its large size
and high demand for biomass.
Facility 2: Enviva Pellets Ahoskie is a
wood pellet facility located near Ahoskie,
North Carolina. In operation since November 2011, the facility is located at a
site that was previously a Georgia Paciﬁc
sawmill. Due to this prior usage, the logging worker base and other wood supply
logistics for this facility are well-established.
The Enviva facility reports a production
output of 350,000 Mg/yr (Wood2Energy
2013) using a mix of approximately 80%
hardwood and 20% softwood feedstock.
The pellets produced at the Ahoskie facility
are shipped to European utilities through
a supply contracts with E.ON, one of the
largest investor owned utilities in the world,
and Electrabel, a subsidiary of GDF SUEZ
Group. The Ahoskie facility is near the
deepwater port of Chesapeake, VA through
which their pellets are exported to the European markets.
Figure 2. The six
facilities chosen
to model land use
change and habitat
impact risks
Wood to Bioenergy Facilities and 75-mile Woodsheds Proposed for Wildlife Impact Analyses
Facility Type
Cellulosic Ethanol (CE)
Electric Generating Unit (EGU)
Coal-Fired EGU Retrofitted for Biomass
Virginia
Wood Pellets (WP)
All other facilities
75-mile Woodshed Buffer
Virginia City Hybrid
Energy Center
South Boston
Energy
!
(
!
(
Enviva Pellets
Ahoskie
!
(
Te n n e s s e e
North Carolina
Carolina Wood
Pellets, LLC
!
(
South Carolina
Georgia
Piedmont
Green Power
!
(
Mississippi
Alabama
Physiographic Province
Appalachian Plateaus
Blue Ridge
Coastal Plain
Interior Low Plateaus
Piedmont
Valley and Ridge
Georgia
Biomass, LLC
!
(
Florida
0
50
100
Miles
200
Data Sources: Southern Environmental Law Center, Esri, USGS

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Forestry Bioenergy in the Southeast United States: Implications for Wildlife Habitat and Biodiversity
Piedmont Facilities
Figure 3. Georgia Biomass, LLC. Source: Google
Figure 4. Enviva Pellets Ahoskie. Source: Google
Facility 3: Piedmont Green Power,
located near Barnesville, Georgia, is a 60.5
MW electric generating unit (Wood2Energy
2013), and one of the few proposed or existing wood based facilities in the southern
Piedmont province. Piedmont Green Power
is a project of Rollcast Energy, Inc. (Errata
– a previous draft of this report erroneously
listed the parent company of this facility).
The unit is intended to provide power to
approximately 40,000 homes. This facility
was identiﬁed as potentially high impact due
to its large biomass demands and its location in the Piedmont.
Facility 4: South Boston Energy, located
near South Boston, Virginia, is a proposed
49.95 MW power facility (Wood2Energy
2013). Proposed feedstocks include wood
wastes, wood chips and slash. This facility is
being constructed with funding from a $90
million USDA loan, and the power will be
purchased by the Northern Virginia Electric
Cooperative (NOVEC) to service approximately 16,000 customers. Current information suggests that this facility will begin
operations in the near future. This facility is
identiﬁed as potentially high impact due to
its large biomass demands, as well as being
one of the few facilities located within the
southeast Piedmont province.
Mountain Facilities
Figure 5. Piedmont Green Power. Source: Google
Facility 5: Carolina Wood Pellets, located
in Otto, North Carolina, is a wood pellet
facility with an estimated production of
68,000 Mg/yr (Wood2Energy 2013). This
facility manufactures hardwood pellets for
domestic home stoves, which are bagged
and sold on the consumer market. The current feedstock is described as scrap wood
from manufacturing, logging and construc-

23.
Forestry Bioenergy in the Southeast United States: Implications for Wildlife Habitat and Biodiversity
Page 23
tion sources. At maximum production
levels, the facility can produce enough wood
pellets to heat 30,000 homes. The facility is
an active installation and has been producing pellets since 2009. This facility presents
an interesting alternative to the other facilities, as it currently uses hardwood residues
as opposed to softwood plantation timber.
The facility is selected for analysis because
of its location in the southern Mountains,
which poses a different set of challenges
and constraints as compared to forestry in
the Coastal Plain and Piedmont provinces.
Facility 6: Virginia City Hybrid Energy
Center, located in St. Paul, Virginia, is a 585
MW electrical generation unit operated by
Dominion Virginia Power (Wood2Energy
2013). This facility is designed to co-ﬁre up
to 20% biomass in its coal fuelled electric
production facility, although is operationally running on a 10% biomass capacity
(~59 MW). This facility is the only co-ﬁred
biomass/coal power facility identiﬁed for
this study. The facility will provide power
for 146,000 homes, 14,600 of which will
be supplied by biomass. The identiﬁed fuel
is wood waste in the form of chips. The
very large biomass demands of this facility, coupled with a sourcing area located in
the southern Mountains, make it potentially
high impact.
Figure 6. South Boston Energy. Source: Google
Note: image predates facility construction at this site
Figure 7. Carolina Wood Pellets. Source: Google
Figure 8. Virginia City Hybrid Energy Center. Source: Google

24.
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Forestry Bioenergy in the Southeast United States: Implications for Wildlife Habitat and Biodiversity
III. INDICATOR SPECIES SELECTION
Authors: Divya Vasudev, Miguel Acevedo, and Robert J. Fletcher, Jr., Department of Wildlife Ecology and Conservation, University of Florida
Identiﬁcation of indicator species
We identiﬁed indicator mammalian, avian,
amphibian and reptilian species for each
bioenergy facility based on a three-step
process. First, we identiﬁed priority species
based on State Wildlife Action Plans of
Georgia, North Carolina, South Carolina
and Virginia (Georgia Department of Natural Resources 2005; South Carolina Department of Natural Resources 2005; North
Carolina Wildlife Resources Commission
2005; Virginia Department of Game and
Inland Fisheries 2005). Species that were of
concern due to their status as a migratory or
game species were given special consideration (e.g., game species: Northern bobwhite Colinus virginianus; migratory species:
Swainson’s warbler Limnothlypis swainsonii).
Second, we obtained range and distribution data of the selected species from the
National Gap Analysis Program (GAP)
(http://gapanalysis.usgs.gov/).
For this exercise, we only used information
on the overall range of the species, while
the distribution of the species within its
range was utilized for the wildlife habitat
modeling (see below). We overlapped the
range of the selected species with a 75-mile
buffer around each facility considered in
this study, thereby identifying those species
located within the vicinity of the selected
facilities on the basis of GAP data. We
preferentially chose taxa that were represented in more than one facility. Lastly, we
used multiple databases to obtain additional
habitat association and conservation status
information on identiﬁed indicator species,
including Animal Diversity Web hosted by
the University of Michigan (http://animaldiversity.ummz.umich.edu), the International Union for the Conservation of Nature
and Natural Resources Red List of Threatened Species (http://www.iucnredlist.org),
and the Cornell Laboratory of Ornithology
(http://www.allaboutbirds.org). We narrowed our search down to approximately
8 candidate species of each taxa, and then
had external reviewers critique the list and
provide suggestions for ﬁnalizing the list of
indicator taxa.
Justiﬁcation for the use of the GAP
database
The National Gap Analysis Program (GAP:
http://gapanalysis.usgs.gov/) is an initiative
of the United States Geological Survey in
partnership with a number of federal and
state agencies, as well as non-governmental
organizations. The GAP database was
developed and has been explicitly applied
for the purpose of identifying regions of
conservation priority and to assess overall
conservation effectiveness (Larson and Sengupta 2004, Rodrigues et al. 2004).
The current GAP database includes a high
resolution (30-m) National Land Cover
map that uses satellite imagery to deﬁne a
seamless set of vegetation and ecosystem
classiﬁcations across the United States
(USGS 2011a). The 2011 National GAP
Land Cover map is widely recognized as
the most detailed national land cover classiﬁcation dataset that maintains consistent
classiﬁcations at a national scale. For this
reason, it is frequently applied for regional

25.
Forestry Bioenergy in the Southeast United States: Implications for Wildlife Habitat and Biodiversity
and national analyses of biodiversity protection, land cover change, renewable energy
assessments, and climate change adaptation
(USGS 2011a).
The GAP database also provides for a
large repository of available information
on species range, distribution and habitat
associations (USGS 2011b). The GAP
wildlife database represents an integrated
collation of current published and expert
knowledge on identiﬁed species. High resolution distribution data in the GAP wildlife
dataset represent both known and predicted
occurrences for a wide range of species at
a 30-m resolution. Predictions of species
distributions are obtained from information
on species habitat associations collated from
published literature and expert opinion. Additionally, elevation, wetland inventories and
other appropriate information are incorporated into predictions of species distribution. It is important to note that GAP data
predicts suitable habitat for species rather
than the probability of occurrence for each
species.
The GAP wildlife database provides information that is directly comparable across
taxa, and is also directly associated with the
National GAP Land Cover classiﬁcation
system. Consequently, this approach provides a standardized and detailed method
for rapidly assessing potential wildlife
vulnerability.
In this study, we used the species distribution models from the GAP database to
provide direct, high-resolution assessments
of wildlife vulnerability under different
sourcing scenarios. Sourcing screen scenarios for biomass conversion or harvest
were developed from the GAP National
Land Cover dataset, with land cover classes
generalized to 100-m (1 hectare) cell sizes.
After developing land cover risk assessment
Page 25
models for each sourcing screen scenario,
we then obtained distribution data for each
selected indicator species from the GAP database. After generalizing wildlife distribution data to 100-m (1 hectare) cell sizes, we
then overlaid these GAP distribution data
for each species with the areas identiﬁed
with each sourcing scenario. This approach
allowed us to calculate the total area of suitable habitat that would be at risk of biomass harvest using a standardized method
with results that are directly comparable.
INDICATOR SPECIES LIST
The following are the mammalian, avian,
amphibian and reptilian indicator species
that resulted from the iterative selection
process (Table 1).
Mammals
1. The eastern spotted skunk Spilogale
putorius is an edge-specialist species,
found at forest-grassland ecotones. The
species is located in the woodsheds
of Georgia Biomass LLC., Piedmont
Green Power, Carolina Wood Pellets
and the Virginia City Hybrid Energy
Center, spanning the states of Georgia, North Carolina and Virginia. The
Figure 9. Eastern spotted skunk
Spilogale putorius Photo credit: NPS

27.
Forestry Bioenergy in the Southeast United States: Implications for Wildlife Habitat and Biodiversity
species is associated with woodland
habitats, such as oak and pine forests,
as well as grassland vegetation, such
as agricultural and pastural lands. The
eastern spotted skunk is a species of
conservation concern in the states of
North Carolina and Virginia (North
Carolina Wildlife Resources Commission 2005; Virginia Department of
Game and Inland Fisheries 2005). In
addition, we chose this species for their
association with ecotones, as well as
their representation in four of the six
chosen facilities. GAP distribution data
are available for this species, and formal
spatial overlays were therefore performed for those facility woodsheds in
which the eastern spotted skunk occurs.
2. The long-tailed weasel Mustela frenata
is a widespread species found in all
woodsheds chosen in this study. They
are a generalist species associated with
a wide variety of habitats and moderately susceptible to land-use change and
habitat fragmentation (Reid & Helgen
2008). Habitats that the long-tailed
weasel inhabits include hardwood and
coniferous forests, pocosin shrublands, cypress swamps and herbaceous
wetlands, grasslands, and urban areas.
Figure 10. Long-tailed weasel Mustela frenata
Photo credit: http://www.ﬂickr.com/photos/willwilson/4429071190/
Page 27
Though widespread, they are a species
of concern in North Carolina, in particular associated with spruce-ﬁr forests
and hardwood forests (North Carolina
Wildlife Resources Commission 2005).
GAP distribution data are available for
this species, and formal spatial overlays
were therefore performed for all facility
woodsheds.
3. The southeastern pocket gopher
Geomys pinetis is located in the state of
Georgia, and is found in the woodsheds
of Georgia Biomass LLC, and the
Piedmont Green Power. The pocket gopher is a species of high conservation
priority in the state of Georgia (Georgia Department of Natural Resources
2005). In addition, pocket gophers are
considered to be ecosystem engineers,
with multiple species utilizing burrows
excavated by the species (Riechman
& Seabloom 2002). The southeastern
pocket gopher is found associated with
pine forests, pine-oak mixed forests and
upland hammock habitats (Lindzey &
Hammerson 2008). GAP distribution
data are not currently available for this
species, and therefore formal spatial
overlays were not performed.
4. The seminole bat Lasiurus seminolus
inhabits the states of Georgia, South
Carolina and North Carolina is found
in three of the six six facility woodsheds considered in this study: Georgia Biomass LLC., Piedmont Green
Power, and Carolina Wood Pellets. The
Seminole bat is listed as a species of
conservation concern, especially associated with woodland habitat in the state
of North Carolina (North Carolina
Wildlife Resources Commission 2005).
These insectivorous species can be
found roosting in pine trees, particu-

28.
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Forestry Bioenergy in the Southeast United States: Implications for Wildlife Habitat and Biodiversity
larly hosting Spanish moss Tillandsia
usneoides. GAP distribution data are not
currently available for this species, and
therefore formal spatial overlays were
not performed.
Birds
1. The northern bobwhite quail Colinus
virginianus is located in all six facility
woodsheds. It is a popular game species, and as such, listed as a high priority
species in the wildlife action plans of
the states of Georgia, North Carolina,
South Carolina and Virginia (Georgia
Department of Natural Resources;
South Carolina Department of Natural
Resources 2005; North Carolina Wildlife Resources Commission 2005; Virginia Department of Game and Inland
Fisheries 2005). The species is found
in pine and xeric woodlands, deciduous forests and agricultural lands. GAP
distribution data are available for northern bobwhite quail, and formal spatial
overlays were therefore performed for
all facility woodsheds.
Figure 11. Bobwhite
Quail Colinus virginianus.
Photo credit: Tom
Wright UF/IFAS
2. The Swainson’s warbler Limnothlypis
swainsonii is a migratory species, whose
seasonal range overlaps with all facility
woodsheds. This insectivore is associated with forested habitats with thick
undergrowth (Graves 2002). These
include oak and mixed bottomland
forests, swamp forests, mesic hardwood forests and Appalachian hemlock
hardwood forests. The Swainson’s
warbler is a species of conservation
concern in the states of South Carolina,
North Carolina and Georgia (Georgia
Department of Natural Resources;
South Carolina Department of Natural Resources 2005; North Carolina
Wildlife Resources Commission 2005).
GAP distribution data are available for
this species, and formal spatial overlays
were therefore performed for all facility
woodsheds.
3. The brown-headed nuthatch Sitta
pusilla is a pine-forest dwelling songbird
found in all woodsheds. The species is
associated with pine forest and savanna
and mixed pine-oak forests, and in addition, ﬂoodplain forests, cypress swamps
and xeric woodlands. The species is
of conservation concern in the states
of Virginia, South Carolina and North
Carolina (South Carolina Department
Figure 12. Swanson’s warbler Limnothlypis
swainsonii. Photo credit: http://www.ﬂickr.com/
photos/juliom/7158750123/

29.
Forestry Bioenergy in the Southeast United States: Implications for Wildlife Habitat and Biodiversity
of Natural Resources 2005; North
Carolina Wildlife Resources Commission 2005; Virginia Department of
Game and Inland Fisheries 2005). GAP
distribution data are available for this
species, and formal spatial overlays
were therefore performed for all facility
woodsheds.
Page 29
4. The prothonotary warbler Protonotaria
citrea is a migratory songbird found
throughout wooded swamps of southeastern United States of America. This
species is associated with ﬂoodplain
forests and other bottomland forests.
Successful breeding is contingent on
the presence of water bodies, and trees
with nesting cavities. GAP distribution
data are available for this species, and
formal spatial overlays were performed
for the Enviva Pellets woodshed.
Amphibians
Figure 13. Brown-headed nuthatch
Sitta pusilla. Photo credit: http://www.ﬂickr.com/
photos/vickisnature/3297971410/
Figure 14. Prothonotary warbler
Colinus virginianus. Photo credit: Jeff Lewis
1. The gopher frog Lithobates capito is a
species endemic to the Southeastern
United States of America. At least two
states list the gopher frog as a species
of conservation concern (Georgia Department of Natural Resources 2005;
South Carolina Department of Natural
Resources 2005). Habitat associations
include longleaf pine and turkey oak
forests and pine ﬂatwoods, where the
species uses pocket gopher and gopher
tortoise Gopherus polyphemus burrows
(Bihovde 2006). Egg masses are laid
in water, and hence permanent water
bodies are essential breeding habitat.
GAP distribution data are available for
Figure 15. Gopher frog Lithobates capito.
Photo credit: Steve A. Johnson.

30.
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Forestry Bioenergy in the Southeast United States: Implications for Wildlife Habitat and Biodiversity
der group of indicator species. Taken
together, the group is found inhabiting
areas in the woodsheds of Piedmont
Green Power, South Boston Energy,
Carolina Wood Pellets, Virginia City
Hybird Energy Center and Enviva Pellets LP. The northern slimy salamander
is listed as a priority species in North
Carolina (North Carolina Wildlife Resources Commission 2005). These salamanders are found in moist woodlands
and upland forests. GAP distribution
data are not currently available for these
species, and therefore formal spatial
overlays were not performed.
this species, and formal spatial overlays were performed for the Georgia
Biomass and Piedmont Green Power
woodsheds.
2. The northern cricket frog Acris crepitans requires permanent water bodies
for their persistence. The distribution
of the species encompasses all facility
woodsheds considered in this study,
with the exception of the Virginia City
Hybrid Energy Center. The species is
of moderate conservation priority in
South Carolina (South Carolina Department of Natural Resources 2005). This
species was chosen for its requirement
for permanent water bodies, such as
ponds, marshes and reservoirs, and
its use of pine woodlands as dispersal
habitat. GAP distribution data are available for this species, and formal spatial
overlays were therefore performed for
all facility woodsheds.
3. We include the white-spotted slimy
salamander Plethodon cylindraceus, the
northern slimy salamander P. glutinosus
and the South Carolina slimy salamander P. variolatus in the slimy salaman-
4.
Mole salamanders Ambystoma spp.,
of interest in our study include the
eastern tiger salamander Ambystoma
tigrinum, found in the Georgia Biomass
woodshed, and the Mabee’s salamander
Ambystoma mabeei, found in the Enviva
Pellets woodshed. The eastern tiger
salamander is a high priority species in
the state of South Carolina (South Carolina Department of Natural Resources
2005), while the Mabee’s salamander is
of priority in the state of North Carolina (North Carolina Wildlife Resources
Commission 2005). The species group
was identiﬁed as an indicator species
as it requires for its survival breeding
ponds and upland woodland habitat
(Madison & Farrand 1998). Bottomland
forests, cypress swamp and ﬂoodplain
forests include habitat the species
inhabits. GAP distribution data are not
currently available for these species, and
therefore formal spatial overlays were
not performed.
5. The three-lined salamander Eurycea
guttolineata is located in all facility woodFigure 16. Northern cricket frog Acris crepitans. Photo credit: http://www.
ﬂickr.com/photos/pcoin/369987905/

31.
Forestry Bioenergy in the Southeast United States: Implications for Wildlife Habitat and Biodiversity
sheds considered in this study except
for the Virginia City Hybrid Energy
Center. The species is of conservation
concern in the state of North Carolina
(North Carolina Wildlife Resources
Commission 2005). The species can be
found in forested ﬂoodplains and moist
woodland habitats (Hammerson 2004).
Thus, emergent vegetation, bottomland
forests, ﬂoodplain forests, streamhead
swamps, and wet shrublands form habitat for the species. GAP distribution
data are not currently available for these
species, and therefore formal spatial
overlays were not performed
Reptiles
1. The timber rattlesnake Crotalus horridus is found in all facility woodsheds.
The species is of conservation concern
in the states of South Carolina, North
Carolina and Virginia (South Carolina
Department of Natural Resources
2005; North Carolina Wildlife Resources Commission 2005; Virginia Department of Game and Inland Fisheries
2005). As its name suggests, this snake
is found inhabiting woodland regions,
including deciduous, coniferous, and
upland forests (Hammerson 2007).
GAP distribution data are available for
this species, and formal spatial overlays
were therefore performed for all facility
woodsheds.
Page 31
high priority (North Carolina Wildlife
Resources Commission 2005). The species is found inhabiting mature pine and
mixed hardwood forests. GAP distribution data are not currently available
for these species, and therefore formal
spatial overlays were not performed.
3. The common ﬁve-lined skink Plestiodon fasciatus is also distributed throughout all facility woodsheds considered
for this study. These rather common
species is found in woodland areas
throughout their range, including pine
forests, swamps, ﬂoodplain forests,
wet shrublands and mixed oak forests.
GAP distribution data are not currently
available for these species, and therefore formal spatial overlays were not
performed.
2. The broad-headed skink Plestiodon
laticeps is distributed extensively in the
states of Georgia, North Carolina,
South Carolina and Virginia, and is
found in all facilities chosen for this
study. The state of North Carolina
lists this skink as a reptilian species of
Figure 17. Timber rattlesnake Crotalus horridus.
Photo credit: Steve A. Johnson

32.
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Forestry Bioenergy in the Southeast United States: Implications for Wildlife Habitat and Biodiversity
IV. SPATIAL MODELING METHODOLOGY
Author: Jason M. Evans, Planning and
Environmental Services Unit, Carl Vinson Institute of Government, University
of Georgia
The land cover risk modeling in this project
is based upon a multi-criteria evaluation
(MCE) decision support framework, as
applied with the IDRISI Selva software platform (Eastman 2012). The MCE process
is based on an integrated assessment of
landscape suitability for achieving a given
objective (e.g., biomass harvest) through
consideration of what are referred to as
“constraints” and “factors.”
Constraints are deﬁned in the IDRISI MCE
process as a Boolean (0, 1) raster input map
variable that has the effect of either allowing or not allowing the given objective to
be sourced from any particular area in the
landscape. For example, input maps of public conservation lands that are managed in a
way that biomass harvest is prohibited take
the form of a constraint. More speciﬁcally,
any areas that are known to be in public
conservation land would be classiﬁed as
unavailable (Boolean value=0), while other
areas would be classiﬁed as potentially available (Boolean value=1).
Factors in the IDRISI MCE process are
deﬁned as map variables that have a continuous effect on landscape suitability for
achieving the given objective. For example,
travel distances from a biomass facility is
modeled as having a continuous effect on
suitability, as shorter distances can be expected to entail less travel cost for biomass
procurement. Although factor variables
may be entered into the IDRISI program
utilizing any range of continuous numbers,
the MCE process requires normalization of
all factor variables into an integer range of
0-255. Values of 0 are generally classiﬁed
as “Least suitable,” while values of 255 are
equivalent to “Most suitable.”
In this project, the ﬁnal MCE integration
of constraints and factors was applied using
a Weighted Linear Combination (WLC)
procedure. The WLC requires applying percentage weights to each normalized factor
map, with the total weighted percentage for
factors equaling 100%. While any cell with a
value of 0 for any constraint is masked as 0,
factor values for all other cells are weighted
and summed to produce a ﬁnal MCE output. Using the WLC on a cell by cell basis,
the MCE is calculated as:
MCE = Σ (Wi * Ri), where
W = Weight % for Factor i; and
R = Raster cell value for Factor i
Constraint Development
A series of three primary constraint factors were deﬁned for the land cover models
across all facilities: 1) woodshed delineation
(0 = areas further than 75 miles network
distance; 1 = areas less than 75 miles network distance); 2) conservation lands (0 =
conservation; 1 = not identiﬁed as conservation); and 3) land cover sourcing screens
(0 = land covers assumed as unavailable; 1
= land covers assumed as available). Cell
resolution for all raster constraint datasets
was set at 100 meters.
Woodshed delineation
The woodshed delineation constraint was
developed through Network Analyst tool

33.
Forestry Bioenergy in the Southeast United States: Implications for Wildlife Habitat and Biodiversity
in ArcGIS10.1. Using the Roads dataset, a
75-mile Service Area boundary shapeﬁle
(Woodshed Delineation) was developed
based on the input point coordinates for
modeled bioenergy facility (see detail below
in Travel distance factor). These 75-mile
Service Areas were then transformed into a
Boolean raster datasets (0 = outside of service area, 1 = inside service area) that cover
rectangular extents deﬁned by the most
extreme latitudes (north-south Y boundary
coordinates) and longitudes (east-west X
boundary coordinates) of the service area
polygon. This constraint was deﬁned as the
Woodshed Delineation.
Conservation lands
A conservation land constraint was developed for each facility through a Union
overlay of at least three map inputs: 1) the
75-mile Woodshed Delineation shapeﬁle;
2) the Federal Lands shapeﬁle as clipped
to the Woodshed Delineation shapeﬁle;
and 3) all state level conservation shapeﬁles, as clipped to the Woodshed Delineation shapeﬁle, for states with at least some
land area located in the 75-mile woodshed
area. The output shapeﬁle from this Union
procedure is described as Conservation
Mask. A new attribute column was added
into the Conservation Mask and given the
name Raster. All areas located in a deﬁned
conservation area assigned the value of 0
for the Raster column, while those not in a
deﬁned conservation area were deﬁned as 1.
The Conservation Mask shapeﬁle was then
transformed into a Boolean raster dataset (0
= conservation land; 1 = not conservation
land) at a 100m cell resolution using the
values in the Raster column.
Two iterations of conservation land constraint were developed for the Carolina
Wood Pellets and Dominion Virginia City
Page 33
Hybrid Energy facilities. The ﬁrst iteration classiﬁed all National Forest lands as
unavailable (Boolean value = 0) for sourcing hardwood biomass production. This
constraint was given the acronym NNF for
“No National Forest.” The second iteration classiﬁed designated Wilderness areas
within National Forests as unavailable for
sourcing woody biomass production, but
assumed that non-Wilderness areas would
be available. This constraint was given the
acronym NFA for “National Forest Allowed.” All other state and federal conservation lands were assumed as unavailable in
both National Forest constraint iterations
for Carolina Wood Pellets and Virginia City
Hybrid Energy.
Land cover sourcing screens
Land cover classiﬁcations within the GAP
Land Cover dataset were used as the basis
for deﬁning a series of sourcing screen
constraints for each facility. To facilitate
computation efﬁciency of spatial models
across large sourcing areas, the GAP Land
Cover data classes, which have an original
cell resolution of 30 meters, were generalized to a cell resolution of 100 meters. The
land cover classiﬁcation of each generalized
cell was deﬁned as the most frequent land
cover class of original resolution contained
within the new raster cell area.
Two facilities were modeled based on an assumption of the dominant feedstock being
provided by pine plantation biomass: Georgia Biomass and Piedmont Green Power.
In addition, the South Boston Energy and
Enviva facilities were modeled as sourcing
some softwood, as well as hardwood. For
the softwood sourcing associated with these
four facilities, a series of ﬁve land cover
sourcing constraint scenarios (i.e., screens)
were developed.

34.
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Forestry Bioenergy in the Southeast United States: Implications for Wildlife Habitat and Biodiversity
 Softwood Screen 1: Deﬁne GAP land
cover class as “Evergreen Plantation
or Managed Pine” as Boolean = 1. All
other land cover classes are deﬁned as
Boolean = 0. This screen was given the
acronym “PO” for “Plantation Only.”
 Softwood Screen 2: GAP land cover
classes “Evergreen Plantation or Managed Pine,” “Harvested Forest – Grass/
Forb Regeneration,” “Harvested Forest
– Shrub Regeneration,” “Disturbed/
Successional – Grass/Forb Regeneration,” “Disturbed Successional – Shrub
Regeneration,” and “Undifferentiated
Barren Land” classiﬁed as Boolean = 1.
All other land cover classes are deﬁned
as Boolean = 0. This screen was given
the acronym “PNP” for “Plantation
and Disturbed, No Pasture.”
 Softwood Screen 3: Include Pasture/
Hay as Boolean = 1, in addition to all
Boolean = 1 classes deﬁned in Screen
2. This screen was given the acronym
“PDP” for “Plantation, Disturbed and
Pasture.”
 Softwood Screen 4: This screen deﬁnes all upland forests and disturbed
forest ecosystems as Boolean = 1, in
addition to all Boolean = 1 classes
deﬁned in Screen 2. Pasture/Hay and
all other land covers are classiﬁed as
Boolean = 0. This screen was given
the acronym “FNP” for “Forests No
Pasture.”
 Softwood Screen 5: This screen is
similar to sourcing Screen 4, with the
exception of deﬁning Pasture/Hay as
Boolean = 1. This screen was given the
acronym of “UPL” for “Uplands.”
All softwood screens were based on the
hard assumption that existing row crop
lands, developed lands, and wetlands are
unavailable for conversion. While some
conversion among these land use types into
plantation pine may be expected to occur
in any woodshed, previous analyses suggest
that these land covers are far less likely to
convert into plantation pine than upland
forests or low intensity pastures (Zhang and
Polyakov 2010). Because detailed statistical
modeling of transitional probabilities at the
ecosystem scale was beyond the scope of
this study, the most parsimonious assumption was to restrict the land cover analysis
to identiﬁed upland forests and non-prime
agricultural lands (i.e., pasture/hay).
Three facilities were modeled as having a
dedicated hardwood feedstock supply: Enviva (80% hardwood), South Boston Energy
(50% hardwood), and Carolina Wood Pellets (100% hardwood). For these facilities,
two scenario constraints were modeled for
hardwood sourcing:
 Hardwood Screen 1: Includes all
forests and disturbed forests in which
hardwood trees may be present as Boolean = 1. Forest types with GAP NVC_
MACRO classiﬁcations of “Longleaf
Pine & Sand Pine Woodland,” “Southeastern North American Ruderal Forest
& Plantation,” and “Wet Longleaf Pine
& Southern Flatwoods” were assumed
as unsuitable for sourcing hardwood
biomass, and thus were classiﬁed as
Boolean = 0. This screen was given the
acronym “HDW” for “Hardwood.”
 Hardwood Screen 2: Similar to Hardwood Screen 1, except that all wetland
and riparian forest are also deﬁned as
Boolean = 0. This screen was given the
acronym “HNW” for “Hardwood No
Wetland.”
The Virginia City Hybrid Energy facility
was modeled similarly to the hardwood
screens, and the high percentage of hardwood forest types in the woodshed makes

35.
Forestry Bioenergy in the Southeast United States: Implications for Wildlife Habitat and Biodiversity
it likely that hardwoods will serve as the
dominant feedstock. However, because
the combustion process may presumably
accept available softwood material, no hard
percentages were set for hardwood to softwood biomass. Natural forest regeneration
to levels of harvestable biomass was further
assumed to extend beyond the assumed
50-year lifetime of the facility, such that the
non-forested Pasture/Hay land cover was
excluded from all sourcing screens.
 Virginia City Hybrid Energy Forestry Screen 1: Deﬁnes all natural,
plantation, and disturbed forest ecosystems, including riparian and bottomland
forests, as Boolean = 1. All other land
covers are deﬁned as Boolean = 0. This
screen was given the acronym “FOR”
for “Forests.”
 Virginia City Hybrid Energy Forestry Screen 2: Similar to Virginia
City Hybrid Energy Forestry Screen
2, except that all wetland and riparian
forest are deﬁned as Boolean = 0. This
screen was given the acronym “FNW”
for “Forests No Wetlands.”
Factor Development
Two primary factors are known to determine the economic viability for bioenergy
facilities to source woody biomass from
particular forestry locations across the
landscape: 1) travel distance for transporting woody biomass from the forestry site
to the biomass facility; and 2) the strength
of demand competition with other wood
users that may also bid for the same given
biomass resource. These two factors were
modeled using similar spatial analyses for
all facilities considered in this study. In
addition, a third factor of environmental
suitability for conversion into plantation
pine forestry was applied for those biomass
facilities that are sourcing softwood from
plantation pine.
Page 35
Travel distance factor
The travel distance factor was derived
through analyses developed with the Network Analyst tool in ArcGIS10.1. Using the
Roads dataset, a Service Area shapeﬁle was
deﬁned using the input point coordinates
for each modeled bioenergy facility. Break
Areas were deﬁned at 1 mile increments
from 1 to 75 miles, and output polygons
were deﬁned as “Rings.” The Service Area
polygon for each facility were then transformed into a continuous raster datasets
(Range = 1, 75), with the raster value
deﬁned from the column attribute deﬁned
as “ToBreak.” Using this approach, all areas
with network distance of 0-1 miles were
thus deﬁned as raster=1, 1-2 miles as raster
= 2… through 74-75 miles as raster = 75.
The output raster dataset was named Travel.
Competition factor
The competition factor was derived through
a chain of GIS analyses that take into account relative landscape demands associated with other facilities that may source
similar types of woody biomass from within
the modeled facility’s 75-mile woodshed.
These competing facilities were assumed to
include other biomass energy facilities (with
facility demand from Wood2Energy 2013)
and pulp mills (with facility demand data
from Bentley and Steppleton 2012). Saw
mills were not modeled as potential competitors due to the higher quality wood and
associated higher prices associated with the
supply of saw timber demand. The full GIS
work ﬂow for the competition analysis is
described in the Competition Figure 18.
While the GIS procedure for deriving the
competition factor involved a complex array of steps, the underlying premise of the
resultant competition factor is that other
woody biomass facilities exert competitive
pressure (C) across the landscape as a direct